SoroushVahidi/scidocs huggingface dataset:
Driver behavior recognition based on deep convolutional neural networks
(2016)

Traffic safety is a severe problem around the world. Many road accidents are normally related with the driver's unsafe driving behavior, e.g. eating while driving. In this work, we propose a vision-based solution to recognize the driver's behavior based on convolutional neural networks. Specifically, given an image, skin-like regions are extracted by Gaussian Mixture Model, which are passed to a deep convolutional neural networks model, namely R*CNN, to generate action labels. The skin-like regions are able to provide abundant semantic information with sufficient discriminative capability. Also, R*CNN is able to select the most informative regions from candidates to facilitate the final action recognition. We tested the proposed methods on Southeast University Driving-posture Dataset and achieve mean Average Precision(mAP) of 97.76% on the dataset which prove the proposed method is effective in drivers's action recognition.
Emotion regulation: affective, cognitive, and social consequences.
(2002)

One of life's great challenges is successfully regulating emotions. Do some emotion regulation strategies have more to recommend them than others? According to Gross's (1998, Review of General Psychology, 2, 271-299) process model of emotion regulation, strategies that act early in the emotion-generative process should have a different profile of consequences than strategies that act later on. This review focuses on two commonly used strategies for down-regulating emotion. The first, reappraisal, comes early in the emotion-generative process. It consists of changing the way a situation is construed so as to decrease its emotional impact. The second, suppression, comes later in the emotion-generative process. It consists of inhibiting the outward signs of inner feelings. Experimental and individual-difference studies find reappraisal is often more effective than suppression. Reappraisal decreases emotion experience and behavioral expression, and has no impact on memory. By contrast, suppression decreases behavioral expression, but fails to decrease emotion experience, and actually impairs memory. Suppression also increases physiological responding for suppressors and their social partners. This review concludes with a consideration of five important directions for future research on emotion regulation processes.
OAuth Demystified for Mobile Application Developers
(2014)

OAuth has become a highly influential protocol due to its swift and wide adoption in the industry. The initial objective of the protocol was specific: it serves the authorization needs for websites. What motivates our work is the realization that the protocol has been significantly re-purposed and re-targeted over the years: (1) all major identity providers, e.g., Facebook, Google and Microsoft, have re-purposed OAuth for user authentication; (2) developers have re-targeted OAuth to the mobile platforms, in addition to the traditional web platform. Therefore, we believe that it is necessary and timely to conduct an in-depth study to demystify OAuth for mobile application developers. Our work consists of two pillars: (1) an in-house study of the OAuth protocol documentation that aims to identify what might be ambiguous or unspecified for mobile developers; (2) a field-study of over 600 popular mobile applications that highlights how well developers fulfill the authentication and authorization goals in practice. The result is really worrisome: among the 149 applications that use OAuth, 89 of them (59.7%) were incorrectly implemented and thus vulnerable. In the paper, we pinpoint the key portions in each OAuth protocol flow that are security critical, but are confusing or unspecified for mobile application developers. We then show several representative cases to concretely explain how real implementations fell into these pitfalls. Our findings have been communicated to vendors of the vulnerable applications. Most vendors positively confirmed the issues, and some have applied fixes. We summarize lessons learned from the study, hoping to provoke further thoughts about clear guidelines for OAuth usage in mobile applications.
Playful public participation in urban planning: A case study for online serious games
(2012)

The aim of this paper is to study the implementation of online games to encourage public participation in urban planning. Its theoretical foundations are based on previous work in public participatory geographical information systems (PP GISs), play and games, with a special focus on serious games. Serious games aim to support learning processes in a new, more playful way. We developed the concept of playful public participation in urban planning, including playful elements such as storytelling, walking and moving, sketching, drawing, and games. A group of students designed an online serious public participatory game entitled NextCampus. The case study used in NextCampus was taken from the real-world question of a possible move of a university campus to a new location in the city of Hamburg, Germany. The development of the serious public participatory game NextCampus resulted in a physical prototype, user interface design, and a computational model of the game. The NextCampus game was tested with the help of two groups of urban planning students and presented to three external experts who provided valuable recommendations for further development. The critical comments questioned the level of complexity involved in such games. The positive comments included recognition of the potential for joy and the play-fulness a game like NextCampus could evoke. Public participatory online applications aim to attract citizens to discuss current issues related to their environment and to improve the process of public participation in general. An integration of geographic information systems (GISs) with public participatory tools represents one of the latest innovations in this area. ways of integrating the new applications into participatory processes and considers which new functionalities and technical characteristics could offer the most benefit to users. In the past, these technologies and other map-based applications were frequently criticized as being too complex for the majority of potential users (Steinmann, Krek, & Blaschke, 2004). New forms of collaboration and technical solutions emerged during the Web 2.0 era. For example , Google Maps and Google Earth can be used by lay users and non-experts without intense training. Recent research on collabo-rative mapping also known as ''geography without geographers'' prior work in PP GIS to include much wider, distributed participation (Hardy, 2008). Despite these new forms of collaboration and innovative technologies , Moody (2007) demonstrates that the use of GIS technology to involve citizens in participatory urban planning does not seem to empower citizens. An important factor in such findings …
Distributed Optimization Using the Primal-Dual Method of Multipliers
(2018)

In this paper, we propose the primal-dual method of multipliers (PDMM) for distributed optimization over a graph. In particular, we optimize a sum of convex functions defined over a graph, where every edge in the graph carries a linear equality constraint. In designing the new algorithm, an augmented primal-dual Lagrangian function is constructed which smoothly captures the graph topology. It is shown that a saddle point of the constructed function provides an optimal solution of the original problem. Further under both the synchronous and asynchronous updating schemes, PDMM has the convergence rate of <inline-formula><tex-math notation="LaTeX">$O(1/K)$</tex-math></inline-formula> (where <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> denotes the iteration index) for general closed, proper, and convex functions. Other properties of PDMM such as convergence speeds versus different parameter-settings and resilience to transmission failure are also investigated through the experiments of distributed averaging.
The use of pit and fissure sealants.
(2002)

This paper reviews key issues of sealant use and methodology and poses recommendations to inform the discussion toward a consensus statement by participants. A comprehensive review of sealant literature, including policy recommendations from previous conferences that reviewed best practices for sealant use, was completed. Building on the review paper by Simonsen and on previous policy statements by dental and public health groups, this paper discusses key questions about sealant use in light of contemporary caries data and cost-benefit analyses. In addition, newest material advancements are reviewed to establish the next step in sealant improvement for young patients.
Physician Burnout and Well-Being: A Systematic Review and Framework for Action.
(2017)

BACKGROUND
Physician burnout in the United States has reached epidemic proportions and is rising rapidly, although burnout in other occupations is stable. Its negative impact is far reaching and includes harm to the burned-out physician, as well as patients, coworkers, family members, close friends, and healthcare organizations.


OBJECTIVE
The purpose of this review is to provide an accurate, current summary of what is known about physician burnout and to develop a framework to reverse its current negative impact, decrease its prevalence, and implement effective organizational and personal interventions.


DATA SOURCES
I completed a comprehensive MEDLINE search of the medical literature from January 1, 2000, through December 28, 2016, related to medical student and physician burnout, stress, depression, suicide ideation, suicide, resiliency, wellness, and well-being. In addition, I selectively reviewed secondary articles, books addressing the relevant issues, and oral presentations at national professional meetings since 2013.


STUDY SELECTION
Healthcare organizations within the United States were studied.


RESULTS
The literature review is presented in 5 sections covering the basics of defining and measuring burnout; its impact, incidence, and causes; and interventions and remediation strategies.


CONCLUSIONS
All US medical students, physicians in training, and practicing physicians are at significant risk of burnout. Its prevalence now exceeds 50%. Burnout is the unintended net result of multiple, highly disruptive changes in society at large, the medical profession, and the healthcare system. Both individual and organizational strategies have been only partially successful in mitigating burnout and in developing resiliency and well-being among physicians. Two highly effective strategies are aligning personal and organizational values and enabling physicians to devote 20% of their work activities to the part of their medical practice that is especially meaningful to them. More research is needed.
Performance Analysis of Alamouti Code MIMO-OFDM Systems for Error Control and IQ Impairments
(2015)

Bit Error Rate (BER) performance is the most important metric of any digital communication system. MIMO-OFDM systems designed with Alamouti Coding technique, using Error Control Coding schemes has been investigated under this study to achieve BER enhancement and scrutinized for Additive White Gaussian Noise (AWGN) Channel with Inphase and Quadrature-phase (IQ) impairments. This paper details out the model developed using NI LabVIEW and discusses simulation results comparing the error control coding RS, RS-CC and Convolution encoding with 4-PSK and 4-QAM. General Terms Bit Error Rate, Space Time Coding (STC).
Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks
(2018)

Alzheimer’s disease is an incurable, progressive neurological brain disorder. Earlier detection of Alzheimer’s disease can help with proper treatment and prevent brain tissue damage. Several statistical and machine learning models have been exploited by researchers for Alzheimer’s disease diagnosis. Analyzing magnetic resonance imaging (MRI) is a common practice for Alzheimer’s disease diagnosis in clinical research. Detection of Alzheimer’s disease is exacting due to the similarity in Alzheimer’s disease MRI data and standard healthy MRI data of older people. Recently, advanced deep learning techniques have successfully demonstrated human-level performance in numerous fields including medical image analysis. We propose a deep convolutional neural network for Alzheimer’s disease diagnosis using brain MRI data analysis. While most of the existing approaches perform binary classification, our model can identify different stages of Alzheimer’s disease and obtains superior performance for early-stage diagnosis. We conducted ample experiments to demonstrate that our proposed model outperformed comparative baselines on the Open Access Series of Imaging Studies dataset.
Future Internet: The Internet of Things Architecture, Possible Applications and Key Challenges
(2012)

The Internet is continuously changing and evolving. The main communication form of present Internet is human-human. The Internet of Things (IoT) can be considered as the future evaluation of the Internet that realizes machine-to-machine (M2M) learning. Thus, IoT provides connectivity for everyone and everything. The IoT embeds some intelligence in Internet-connected objects to communicate, exchange information, take decisions, invoke actions and provide amazing services. This paper addresses the existing development trends, the generic architecture of IoT, its distinguishing features and possible future applications. This paper also forecast the key challenges associated with the development of IoT. The IoT is getting increasing popularity for academia, industry as well as government that has the potential to bring significant personal, professional and economic benefits.
Towards smart city: M2M communications with software agent intelligence
(2012)

Recent advances in the fields of wireless technology have exhibited a strong potential and tendency on improving human life by means of ubiquitous communications devices that enable smart, distributed services. In fact, traditional human to human (H2H) communications are gradually falling behind the scale of necessity. Consequently, machine to machine (M2M) communications have surpassed H2H, thus drawing significant interest from industry and the research community recently. This paper first presents a four-layer architecture for internet of things (IoT). Based on this architecture, we employ the second generation RFID technology to propose a novel intelligent system for M2M communications.
Internet of things capability and alliance: Entrepreneurial orientation, market orientation and product and process innovation
(2016)

Internet of things capability and alliance: entrepreneurial orientation, market orientation and product and process innovation Xiaoyu Yu Bang Nguyen Yi Chen Article information: To cite this document: Xiaoyu Yu Bang Nguyen Yi Chen , (2016),"Internet of things capability and alliance: entrepreneurial orientation, market orientation and product and process innovation", Internet Research, Vol. 26 Iss 2 pp. Permanent link to this document: http://dx.doi.org/10.1108/IntR-10-2014-0265
DepSys: Dependency aware integration of cyber-physical systems for smart homes
(2014)

As sensor and actuator networks mature, they become a core utility of smart homes like electricity and water and enable the running of many CPS applications. Like other Cyber-Physical Systems (CPSs), when a number of applications share physical world entities, it raises many systems of systems interdependency problems. Such problems arise in the cyber part mainly because each application has assumptions on the physical world entities without knowing how other applications work. In this work, we propose DepSys, a utility sensing and actuation infrastructure for smart homes that provides comprehensive strategies to specify, detect, and resolve conflicts in a home setting. Based on real home data, we demonstrate the severity of conflicts when multiple CPSs are integrated and the significant ability of detecting and resolving such conflicts using DepSys.
Scaling Distributed Machine Learning with System and Algorithm Co-design
(2016)

Due to the rapid growth of data and the ever increasing model complexity, which often manifests itself in the large number of model parameters, today, many important machine learning problems cannot be efficiently solved by a single machine. Distributed optimization and inference is becoming more and more inevitable for solving large scale machine learning problems in both academia and industry. However, obtaining an efficient distributed implementation of an algorithm, is far from trivial. Both intensive computational workloads and the volume of data communication demand careful design of distributed computation systems and distributed machine learning algorithms. In this thesis, we focus on the co-design of distributed computing systems and distributed optimization algorithms that are specialized for large machine learning problems. In the first part, we propose two distributed computing frameworks: Parameter Server, a distributed machine learning framework that features efficient data communication between the machines; MXNet, a multi-language library that aims to simplify the development of deep neural network algorithms. We have witnessed the wide adoption of the two proposed systems in the past two years. They have enabled and will continue to enable more people to harness the power of distributed computing to design efficient large-scale machine learning applications. In the second part, we examine a number of distributed optimization problems in machine learning, leveraging the two computing platforms. We present new methods to accelerate the training process, such as data partitioning with better locality properties, communication friendly optimization methods, and more compact statistical models. We implement the new algorithms on the two systems and test on large scale real data sets. We successfully demonstrate that careful co-design of computing systems and learning algorithms can greatly accelerate large scale distributed machine learning.
Hardware-software integrated approaches to defend against software cache-based side channel attacks
(2009)

Software cache-based side channel attacks present serious threats to modern computer systems. Using caches as a side channel, these attacks are able to derive secret keys used in cryptographic operations through legitimate activities. Among existing countermeasures, software solutions are typically application specific and incur substantial performance overhead. Recent hardware proposals including the Partition-Locked cache (PLcache) and Random-Permutation cache (RPcache) [23], although very effective in reducing performance overhead while enhancing the security level, may still be vulnerable to advanced cache attacks. In this paper, we propose three hardware-software approaches to defend against software cache-based attacks - they present different tradeoffs between hardware complexity and performance overhead. First, we propose to use preloading to secure the PLcache. Second, we leverage informing loads, which is a lightweight architectural support originally proposed to improve memory performance, to protect the RPcache. Third, we propose novel software permutation to replace the random permutation hardware in the RPcache. This way, regular caches can be protected with hardware support for informing loads. In our experiments, we analyze various processor models for their vulnerability to cache attacks and demonstrate that even to the processor model that is most vulnerable to cache attacks, our proposed software-hardware integrated schemes provide strong security protection.
Modelling Interaction of Sentence Pair with Coupled-LSTMs
(2016)

Recently, there is rising interest in modelling the interactions of two sentences with deep neural networks. However, most of the existing methods encode two sequences with separate encoders, in which a sentence is encoded with little or no information from the other sentence. In this paper, we propose a deep architecture to model the strong interaction of sentence pair with two coupled-LSTMs. Specifically, we introduce two coupled ways to model the interdependences of two LSTMs, coupling the local contextualized interactions of two sentences. We then aggregate these interactions and use a dynamic pooling to select the most informative features. Experiments on two very large datasets demonstrate the efficacy of our proposed architecture and its superiority to state-ofthe-art methods.
An introduction to the Rasch measurement model: an example using the Hospital Anxiety and Depression Scale (HADS).
(2007)

OBJECTIVES
To demonstrate the use of Rasch analysis by assessing the appropriateness of utilizing the Hospital Anxiety and Depression Scale (HADS) total score (HADS-14) as a measure of psychological distress.


DESIGN
Cross-sectional, using Rasch analysis.


METHODS
The HADS was administered to 296 patients attending an out-patient musculoskeletal rehabilitation program. Rasch analysis was conducted using RUMM2020 software to assess the overall fit of the model, the response scale used, individual item fit, differential item functioning (DIF) and person separation.


RESULTS
Rasch analysis supported the viability of the HADS-14 as a measure of psychological distress. It showed good person separation, little disordering of the thresholds and no evidence of DIE One anxiety item (item 11) showed some misfit to the model. The residuals patterned into the two subscales (anxiety and depression), but the person estimate derived from these two subscales was not statistically different to that derived from all items taken together, supporting the assumption of unidimensionality. A cut-point of 12 on the HADS-14 identified all cases that were classified as both anxious and depressed on the original individual HADS subscales.


CONCLUSIONS
The results of Rasch analysis support the use of the HADS-14 as a global measure of psychological distress. The study demonstrates the usefulness of Rasch analysis in assessing the psychometric properties of a scale and suggests that further use of this technique to assess the HADS-14 in other clinical groups is warranted.
Snapshot Image Mapping Spectrometer (IMS) with high sampling density for hyperspectral microscopy
(2010)

A snapshot Image Mapping Spectrometer (IMS) with high sampling density is developed for hyperspectral microscopy, measuring a datacube of dimensions 285 x 285 x 60 (x, y, lambda). The spatial resolution is approximately 0.45 microm with a FOV of 100 x 100 microm(2). The measured spectrum is from 450 nm to 650 nm and is sampled by 60 spectral channels with average sampling interval approximately 3.3 nm. The channel's spectral resolution is approximately 8nm. The spectral imaging results demonstrate the potential of the IMS for real-time cellular fluorescence imaging.
Gain enhancement of dual-band antenna using square loop FSS
(2017)

This paper presents a high gain dual-band microstrip antenna. The antenna operates in 9.25 and 11 GHz (X-band). High gain of antenna is achieved by employing a FSS as superstrate which is formed by the 5 × 5 array of square loops. The square loop FSS resonance is varied and its influence on the antenna behavior is analyzed. Analyze of the simulated antenna gain and return loss are shown. The gain improvement depends on the resonance of the FSS superstrate. The higher gain obtained is 6 dB at 11 GHz while maintaining a good matching level for the first resonance.